r descr_models("mlp", "keras")
defaults <- tibble::tibble(parsnip = c("hidden_units", "penalty", "dropout", "epochs", "activation"), default = c("5L", "0.0", "0.0", "20L", "'softmax'")) param <- mlp() %>% set_engine("keras") %>% make_parameter_list(defaults)
This model has r nrow(param)
tuning parameters:
param$item
mlp( hidden_units = integer(1), penalty = double(1), dropout = double(1), epochs = integer(1), activation = character(1) ) %>% set_engine("keras") %>% set_mode("regression") %>% translate()
mlp( hidden_units = integer(1), penalty = double(1), dropout = double(1), epochs = integer(1), activation = character(1) ) %>% set_engine("keras") %>% set_mode("classification") %>% translate()
The "Fitting and Predicting with parsnip" article contains examples for mlp()
with the "keras"
engine.
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